Investigating the impact of dynamic merge control strategies on driving behavior on rural and urban expressways – A driving simulator study

Abstract

Harsh decelerations and abrupt lane changes of drivers on the outer expressway lane in response to merging platoon vehicles from on-ramps increase the crash-and congestion risk on expressways. Several merge control approaches are worldwide implemented. However, there is a gap in knowledge which driving behaviors determine whether a particular merge control approach is best for a rural or urban expressway. This study tests a number of dynamic merge control strategies, such as lane control signalization (eventually combined with variable speed limits (VSL), on a 4-lane urban and rural expressway to measure the behavioral responses of drivers being directly exposed to platoon merging from on-ramps. Subsequently, a comparison was made with the driving behaviors in response to static merge control (e.g. merge warning signs or road marking treatment). The driving behavior of 66 drivers from the State of Qatar was recorded in a driving simulator and analyzed by means of a within-subject repeated measures analysis with univariate statistics. The results suggest that dynamic merge control is more effective for rural expressways considering the higher traffic speeds. The earlier lane changes contributed to smooth maneuvers and gradual speed reductions on the rural expressway and improved safe driving behavior as compared to static merge control. In contrast, the dynamic merge control did not deliver additional safety benefits on urban expressways and can be substituted by a low-cost static merge control approach. Policymakers aiming to improve traffic safety at expressway merging sections are advised to take the speed characteristics of the local expressway into account before implementing dynamic merge control.

The Influence of Individual Differences on Diverging Behavior at the Weaving Sections of an Urban Expressway

“…With respect to the mean maximum longitudinal deceleration, all types of drivers' mean maximum longitudinal deceleration decreased as the weaving length increased; however, no significant differences were found in mean maximum longitudinal deceleration between 100, 150 and 200 m of weaving length for the same type of drivers (F C (2,42) = 0.528, P = 0.593 > 0.05; F N (2,42) = 0.410, P = 0.666 > 0.05; F A (2,42) = 0.244, P = 0.784 > 0.05) or between different types of driver under the same weaving length (F 100 (2,42) = 0.117, P = 0.890 > 0.05; F 150 (2,42) = 0.493, P = 0.614 > 0.05; F 200 (2,42) = 0.362, P = 0.698 > 0.05). It seems that a longer weaving length might increase the risk of a crash, due to higher driving speed and deceleration (Reinolsmann, 2019;Ma,2020) [37,42]. This result is in agreement with a previous study by Yuan [23].…”

“…A set of driving behavior parameters was analyzed: mean speed (in km/h), mean longitudinal acceleration (s/m 2 ) and the standard deviation of lateral acceleration (m/s 2 ) (SD of lateral acceleration) (Ariën, 2013;Reinolsmann, 2019) [36,37]. Besides this, the lanechange position and lane-changing duration time were measured and analyzed (Toledo, 2007) [38].…”

Experimental Investigation into Driver Behavior along Curved and Parallel Diverging Terminals of Exit Interchange Ramps

“…This evidence confirms that many drivers are reluctant to decelerate correctly. Other studies have shown that the terminal geometry (i.e., type, width, and length) affects the operational and safety performance of such facilities (6)(7)(8)(9). Calvi et al (6) demonstrated that the choice of designing a tapered or parallel lane significantly affects the speeds of diverging drivers, resulting in problematic forms of interaction with those drivers proceeding straight on the motorway.…”

Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study

“…For analyzing drivers' stopping behavior, repeated measures analysis of variance ANOVA is often applied to analyze speed and acceleration differences between tested conditions (Ariën et al, 2013;Calvi, 2018;Charlton et al, 2018;Hussain et al, 2018;Reinolsmann et al, 2019). However, in this study it was not appropriate to conduct repeated measures ANOVA tests, as for different conditions some of the drivers stopped at the intersection while other passed.…”